Pattern learning and recognition has a number of potentially important applications, including computer recognition of spoken words, robotics vision and guidance and scene classification. Most existing pattern recognition devices are based on serial computational architectures. They involve the use of algorithms of feature extraction and comparison or correlation of extracted features from an input pattern with a store of archtypal features in a memory. Their shortcomings include the lack of robust ability to generalize from incomplete, noisy, or distorted features; lack of versatility in assembly of features into whole patterns, excessive time for search; lack of robust rules for association; and the requirement for an exhaustive search to report on a novel pattern which is not in the memory.
More recently, connected network devices designed for pattern learning and recognition have been proposed. Generally, these devices are composed of an array of processing units having extensive feedback interconnections between pairs of units, where the strength of the individual feedback connections determines the learned state of the device. When an N-dimensional input vector, representing a pattern to be recognized, is applied to the device, the system is driven from an unstable initial state to an unstable final state which represents one of the one or more possible point attractor states of the device. That is, the feedback connections define one or more basins to which the system is driven with any input, this basin representing one of the possible patterns or "solutions" recognized by the device.
The capabilities of these devices are not yet fully explored, but they have a number of limitations. In terms of physical structure, the feedback connections between pairs of units severely limits the practical size of the array, since an N unit array can have up to N.sup.2 pairwise connections. Secondly, changing the values of the feedback connections, to create a new learned state, requires relatively complex variable resistance or variable gain elements which complicate construction, by very large systems integration (VLSI) techniques.
The requirement to go full "on" and full "off" requires stopping and restarting the device for each new input. The steady d.c. signal outputs are unstable and difficult to control and adjust in analog systems. The devices need an external source of noise to minimize selection bias and hang-ups on local minima, and there may be a need to lower or remove noise by independent controls during the convergence process.